feat: 切换后端至PaddleOCR-NCNN，切换工程为CMake
1.项目后端整体迁移至PaddleOCR-NCNN算法，已通过基本的兼容性测试 2.工程改为使用CMake组织，后续为了更好地兼容第三方库，不再提供QMake工程 3.重整权利声明文件，重整代码工程，确保最小化侵权风险 Log: 切换后端至PaddleOCR-NCNN，切换工程为CMake Change-Id: I4d5d2c5d37505a4a24b389b1a4c5d12f17bfa38c
This commit is contained in:
wangzhengyang
144
3rdparty/opencv-4.5.4/samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java
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144
3rdparty/opencv-4.5.4/samples/java/tutorial_code/ml/introduction_to_pca/IntroductionToPCADemo.java
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import java.util.ArrayList;
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import java.util.List;
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import org.opencv.core.Core;
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import org.opencv.core.CvType;
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import org.opencv.core.Mat;
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import org.opencv.core.MatOfPoint;
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import org.opencv.core.Point;
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import org.opencv.core.Scalar;
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import org.opencv.highgui.HighGui;
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import org.opencv.imgcodecs.Imgcodecs;
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import org.opencv.imgproc.Imgproc;
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//This program demonstrates how to use OpenCV PCA to extract the orientation of an object.
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class IntroductionToPCA {
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private void drawAxis(Mat img, Point p_, Point q_, Scalar colour, float scale) {
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Point p = new Point(p_.x, p_.y);
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Point q = new Point(q_.x, q_.y);
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//! [visualization1]
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double angle = Math.atan2(p.y - q.y, p.x - q.x); // angle in radians
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double hypotenuse = Math.sqrt((p.y - q.y) * (p.y - q.y) + (p.x - q.x) * (p.x - q.x));
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// Here we lengthen the arrow by a factor of scale
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q.x = (int) (p.x - scale * hypotenuse * Math.cos(angle));
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q.y = (int) (p.y - scale * hypotenuse * Math.sin(angle));
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Imgproc.line(img, p, q, colour, 1, Imgproc.LINE_AA, 0);
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// create the arrow hooks
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p.x = (int) (q.x + 9 * Math.cos(angle + Math.PI / 4));
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p.y = (int) (q.y + 9 * Math.sin(angle + Math.PI / 4));
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Imgproc.line(img, p, q, colour, 1, Imgproc.LINE_AA, 0);
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p.x = (int) (q.x + 9 * Math.cos(angle - Math.PI / 4));
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p.y = (int) (q.y + 9 * Math.sin(angle - Math.PI / 4));
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Imgproc.line(img, p, q, colour, 1, Imgproc.LINE_AA, 0);
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//! [visualization1]
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}
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private double getOrientation(MatOfPoint ptsMat, Mat img) {
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List<Point> pts = ptsMat.toList();
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//! [pca]
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// Construct a buffer used by the pca analysis
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int sz = pts.size();
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Mat dataPts = new Mat(sz, 2, CvType.CV_64F);
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double[] dataPtsData = new double[(int) (dataPts.total() * dataPts.channels())];
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for (int i = 0; i < dataPts.rows(); i++) {
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dataPtsData[i * dataPts.cols()] = pts.get(i).x;
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dataPtsData[i * dataPts.cols() + 1] = pts.get(i).y;
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}
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dataPts.put(0, 0, dataPtsData);
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// Perform PCA analysis
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Mat mean = new Mat();
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Mat eigenvectors = new Mat();
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Mat eigenvalues = new Mat();
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Core.PCACompute2(dataPts, mean, eigenvectors, eigenvalues);
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double[] meanData = new double[(int) (mean.total() * mean.channels())];
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mean.get(0, 0, meanData);
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// Store the center of the object
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Point cntr = new Point(meanData[0], meanData[1]);
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// Store the eigenvalues and eigenvectors
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double[] eigenvectorsData = new double[(int) (eigenvectors.total() * eigenvectors.channels())];
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double[] eigenvaluesData = new double[(int) (eigenvalues.total() * eigenvalues.channels())];
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eigenvectors.get(0, 0, eigenvectorsData);
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eigenvalues.get(0, 0, eigenvaluesData);
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//! [pca]
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//! [visualization]
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// Draw the principal components
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Imgproc.circle(img, cntr, 3, new Scalar(255, 0, 255), 2);
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Point p1 = new Point(cntr.x + 0.02 * eigenvectorsData[0] * eigenvaluesData[0],
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cntr.y + 0.02 * eigenvectorsData[1] * eigenvaluesData[0]);
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Point p2 = new Point(cntr.x - 0.02 * eigenvectorsData[2] * eigenvaluesData[1],
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cntr.y - 0.02 * eigenvectorsData[3] * eigenvaluesData[1]);
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drawAxis(img, cntr, p1, new Scalar(0, 255, 0), 1);
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drawAxis(img, cntr, p2, new Scalar(255, 255, 0), 5);
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double angle = Math.atan2(eigenvectorsData[1], eigenvectorsData[0]); // orientation in radians
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//! [visualization]
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return angle;
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}
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public void run(String[] args) {
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//! [pre-process]
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// Load image
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String filename = args.length > 0 ? args[0] : "../data/pca_test1.jpg";
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Mat src = Imgcodecs.imread(filename);
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// Check if image is loaded successfully
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if (src.empty()) {
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System.err.println("Cannot read image: " + filename);
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System.exit(0);
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}
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Mat srcOriginal = src.clone();
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HighGui.imshow("src", srcOriginal);
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// Convert image to grayscale
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Mat gray = new Mat();
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Imgproc.cvtColor(src, gray, Imgproc.COLOR_BGR2GRAY);
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// Convert image to binary
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Mat bw = new Mat();
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Imgproc.threshold(gray, bw, 50, 255, Imgproc.THRESH_BINARY | Imgproc.THRESH_OTSU);
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//! [pre-process]
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//! [contours]
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// Find all the contours in the thresholded image
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List<MatOfPoint> contours = new ArrayList<>();
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Mat hierarchy = new Mat();
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Imgproc.findContours(bw, contours, hierarchy, Imgproc.RETR_LIST, Imgproc.CHAIN_APPROX_NONE);
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for (int i = 0; i < contours.size(); i++) {
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// Calculate the area of each contour
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double area = Imgproc.contourArea(contours.get(i));
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// Ignore contours that are too small or too large
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if (area < 1e2 || 1e5 < area)
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continue;
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// Draw each contour only for visualisation purposes
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Imgproc.drawContours(src, contours, i, new Scalar(0, 0, 255), 2);
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// Find the orientation of each shape
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getOrientation(contours.get(i), src);
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}
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//! [contours]
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HighGui.imshow("output", src);
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HighGui.waitKey();
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System.exit(0);
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}
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}
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public class IntroductionToPCADemo {
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public static void main(String[] args) {
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// Load the native OpenCV library
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System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
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new IntroductionToPCA().run(args);
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}
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}
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import org.opencv.core.Core;
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import org.opencv.core.CvType;
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import org.opencv.core.Mat;
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import org.opencv.core.Point;
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import org.opencv.core.Scalar;
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import org.opencv.core.TermCriteria;
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import org.opencv.highgui.HighGui;
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import org.opencv.imgcodecs.Imgcodecs;
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import org.opencv.imgproc.Imgproc;
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import org.opencv.ml.Ml;
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import org.opencv.ml.SVM;
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public class IntroductionToSVMDemo {
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public static void main(String[] args) {
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// Load the native OpenCV library
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System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
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// Set up training data
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//! [setup1]
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int[] labels = { 1, -1, -1, -1 };
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float[] trainingData = { 501, 10, 255, 10, 501, 255, 10, 501 };
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//! [setup1]
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//! [setup2]
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Mat trainingDataMat = new Mat(4, 2, CvType.CV_32FC1);
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trainingDataMat.put(0, 0, trainingData);
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Mat labelsMat = new Mat(4, 1, CvType.CV_32SC1);
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labelsMat.put(0, 0, labels);
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//! [setup2]
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// Train the SVM
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//! [init]
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SVM svm = SVM.create();
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svm.setType(SVM.C_SVC);
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svm.setKernel(SVM.LINEAR);
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svm.setTermCriteria(new TermCriteria(TermCriteria.MAX_ITER, 100, 1e-6));
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//! [init]
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//! [train]
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svm.train(trainingDataMat, Ml.ROW_SAMPLE, labelsMat);
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//! [train]
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// Data for visual representation
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int width = 512, height = 512;
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Mat image = Mat.zeros(height, width, CvType.CV_8UC3);
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// Show the decision regions given by the SVM
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//! [show]
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byte[] imageData = new byte[(int) (image.total() * image.channels())];
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Mat sampleMat = new Mat(1, 2, CvType.CV_32F);
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float[] sampleMatData = new float[(int) (sampleMat.total() * sampleMat.channels())];
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for (int i = 0; i < image.rows(); i++) {
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for (int j = 0; j < image.cols(); j++) {
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sampleMatData[0] = j;
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sampleMatData[1] = i;
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sampleMat.put(0, 0, sampleMatData);
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float response = svm.predict(sampleMat);
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if (response == 1) {
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imageData[(i * image.cols() + j) * image.channels()] = 0;
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imageData[(i * image.cols() + j) * image.channels() + 1] = (byte) 255;
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imageData[(i * image.cols() + j) * image.channels() + 2] = 0;
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} else if (response == -1) {
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imageData[(i * image.cols() + j) * image.channels()] = (byte) 255;
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imageData[(i * image.cols() + j) * image.channels() + 1] = 0;
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imageData[(i * image.cols() + j) * image.channels() + 2] = 0;
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}
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}
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}
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image.put(0, 0, imageData);
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//! [show]
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// Show the training data
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//! [show_data]
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int thickness = -1;
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int lineType = Imgproc.LINE_8;
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Imgproc.circle(image, new Point(501, 10), 5, new Scalar(0, 0, 0), thickness, lineType, 0);
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Imgproc.circle(image, new Point(255, 10), 5, new Scalar(255, 255, 255), thickness, lineType, 0);
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Imgproc.circle(image, new Point(501, 255), 5, new Scalar(255, 255, 255), thickness, lineType, 0);
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Imgproc.circle(image, new Point(10, 501), 5, new Scalar(255, 255, 255), thickness, lineType, 0);
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//! [show_data]
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// Show support vectors
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//! [show_vectors]
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thickness = 2;
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Mat sv = svm.getUncompressedSupportVectors();
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float[] svData = new float[(int) (sv.total() * sv.channels())];
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sv.get(0, 0, svData);
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for (int i = 0; i < sv.rows(); ++i) {
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Imgproc.circle(image, new Point(svData[i * sv.cols()], svData[i * sv.cols() + 1]), 6,
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new Scalar(128, 128, 128), thickness, lineType, 0);
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}
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//! [show_vectors]
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Imgcodecs.imwrite("result.png", image); // save the image
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HighGui.imshow("SVM Simple Example", image); // show it to the user
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HighGui.waitKey();
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System.exit(0);
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}
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}
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186
3rdparty/opencv-4.5.4/samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java
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3rdparty/opencv-4.5.4/samples/java/tutorial_code/ml/non_linear_svms/NonLinearSVMsDemo.java
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import java.util.Random;
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import org.opencv.core.Core;
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import org.opencv.core.CvType;
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import org.opencv.core.Mat;
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import org.opencv.core.Point;
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import org.opencv.core.Scalar;
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import org.opencv.core.TermCriteria;
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import org.opencv.highgui.HighGui;
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import org.opencv.imgcodecs.Imgcodecs;
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import org.opencv.imgproc.Imgproc;
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import org.opencv.ml.Ml;
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import org.opencv.ml.SVM;
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public class NonLinearSVMsDemo {
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public static final int NTRAINING_SAMPLES = 100;
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public static final float FRAC_LINEAR_SEP = 0.9f;
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public static void main(String[] args) {
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// Load the native OpenCV library
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System.loadLibrary(Core.NATIVE_LIBRARY_NAME);
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System.out.println("\n--------------------------------------------------------------------------");
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System.out.println("This program shows Support Vector Machines for Non-Linearly Separable Data. ");
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System.out.println("--------------------------------------------------------------------------\n");
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// Data for visual representation
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int width = 512, height = 512;
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Mat I = Mat.zeros(height, width, CvType.CV_8UC3);
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// --------------------- 1. Set up training data randomly---------------------------------------
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Mat trainData = new Mat(2 * NTRAINING_SAMPLES, 2, CvType.CV_32F);
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Mat labels = new Mat(2 * NTRAINING_SAMPLES, 1, CvType.CV_32S);
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Random rng = new Random(100); // Random value generation class
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// Set up the linearly separable part of the training data
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int nLinearSamples = (int) (FRAC_LINEAR_SEP * NTRAINING_SAMPLES);
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//! [setup1]
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// Generate random points for the class 1
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Mat trainClass = trainData.rowRange(0, nLinearSamples);
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// The x coordinate of the points is in [0, 0.4)
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Mat c = trainClass.colRange(0, 1);
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float[] cData = new float[(int) (c.total() * c.channels())];
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double[] cDataDbl = rng.doubles(cData.length, 0, 0.4f * width).toArray();
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for (int i = 0; i < cData.length; i++) {
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cData[i] = (float) cDataDbl[i];
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}
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c.put(0, 0, cData);
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// The y coordinate of the points is in [0, 1)
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c = trainClass.colRange(1, 2);
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cData = new float[(int) (c.total() * c.channels())];
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cDataDbl = rng.doubles(cData.length, 0, height).toArray();
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for (int i = 0; i < cData.length; i++) {
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cData[i] = (float) cDataDbl[i];
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}
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c.put(0, 0, cData);
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// Generate random points for the class 2
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trainClass = trainData.rowRange(2 * NTRAINING_SAMPLES - nLinearSamples, 2 * NTRAINING_SAMPLES);
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// The x coordinate of the points is in [0.6, 1]
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c = trainClass.colRange(0, 1);
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cData = new float[(int) (c.total() * c.channels())];
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cDataDbl = rng.doubles(cData.length, 0.6 * width, width).toArray();
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for (int i = 0; i < cData.length; i++) {
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cData[i] = (float) cDataDbl[i];
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}
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c.put(0, 0, cData);
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// The y coordinate of the points is in [0, 1)
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c = trainClass.colRange(1, 2);
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cData = new float[(int) (c.total() * c.channels())];
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cDataDbl = rng.doubles(cData.length, 0, height).toArray();
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for (int i = 0; i < cData.length; i++) {
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cData[i] = (float) cDataDbl[i];
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}
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c.put(0, 0, cData);
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//! [setup1]
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// ------------------ Set up the non-linearly separable part of the training data ---------------
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//! [setup2]
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// Generate random points for the classes 1 and 2
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trainClass = trainData.rowRange(nLinearSamples, 2 * NTRAINING_SAMPLES - nLinearSamples);
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// The x coordinate of the points is in [0.4, 0.6)
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c = trainClass.colRange(0, 1);
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cData = new float[(int) (c.total() * c.channels())];
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cDataDbl = rng.doubles(cData.length, 0.4 * width, 0.6 * width).toArray();
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for (int i = 0; i < cData.length; i++) {
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cData[i] = (float) cDataDbl[i];
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}
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c.put(0, 0, cData);
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// The y coordinate of the points is in [0, 1)
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c = trainClass.colRange(1, 2);
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cData = new float[(int) (c.total() * c.channels())];
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cDataDbl = rng.doubles(cData.length, 0, height).toArray();
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for (int i = 0; i < cData.length; i++) {
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cData[i] = (float) cDataDbl[i];
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}
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c.put(0, 0, cData);
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//! [setup2]
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// ------------------------- Set up the labels for the classes---------------------------------
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labels.rowRange(0, NTRAINING_SAMPLES).setTo(new Scalar(1)); // Class 1
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labels.rowRange(NTRAINING_SAMPLES, 2 * NTRAINING_SAMPLES).setTo(new Scalar(2)); // Class 2
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// ------------------------ 2. Set up the support vector machines parameters--------------------
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System.out.println("Starting training process");
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//! [init]
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SVM svm = SVM.create();
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svm.setType(SVM.C_SVC);
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svm.setC(0.1);
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svm.setKernel(SVM.LINEAR);
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svm.setTermCriteria(new TermCriteria(TermCriteria.MAX_ITER, (int) 1e7, 1e-6));
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//! [init]
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// ------------------------ 3. Train the svm----------------------------------------------------
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//! [train]
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svm.train(trainData, Ml.ROW_SAMPLE, labels);
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//! [train]
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System.out.println("Finished training process");
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// ------------------------ 4. Show the decision regions----------------------------------------
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//! [show]
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byte[] IData = new byte[(int) (I.total() * I.channels())];
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Mat sampleMat = new Mat(1, 2, CvType.CV_32F);
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float[] sampleMatData = new float[(int) (sampleMat.total() * sampleMat.channels())];
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for (int i = 0; i < I.rows(); i++) {
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for (int j = 0; j < I.cols(); j++) {
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sampleMatData[0] = j;
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sampleMatData[1] = i;
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sampleMat.put(0, 0, sampleMatData);
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float response = svm.predict(sampleMat);
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if (response == 1) {
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IData[(i * I.cols() + j) * I.channels()] = 0;
|
||||
IData[(i * I.cols() + j) * I.channels() + 1] = 100;
|
||||
IData[(i * I.cols() + j) * I.channels() + 2] = 0;
|
||||
} else if (response == 2) {
|
||||
IData[(i * I.cols() + j) * I.channels()] = 100;
|
||||
IData[(i * I.cols() + j) * I.channels() + 1] = 0;
|
||||
IData[(i * I.cols() + j) * I.channels() + 2] = 0;
|
||||
}
|
||||
}
|
||||
}
|
||||
I.put(0, 0, IData);
|
||||
//! [show]
|
||||
|
||||
// ----------------------- 5. Show the training data--------------------------------------------
|
||||
//! [show_data]
|
||||
int thick = -1;
|
||||
int lineType = Imgproc.LINE_8;
|
||||
float px, py;
|
||||
// Class 1
|
||||
float[] trainDataData = new float[(int) (trainData.total() * trainData.channels())];
|
||||
trainData.get(0, 0, trainDataData);
|
||||
for (int i = 0; i < NTRAINING_SAMPLES; i++) {
|
||||
px = trainDataData[i * trainData.cols()];
|
||||
py = trainDataData[i * trainData.cols() + 1];
|
||||
Imgproc.circle(I, new Point(px, py), 3, new Scalar(0, 255, 0), thick, lineType, 0);
|
||||
}
|
||||
// Class 2
|
||||
for (int i = NTRAINING_SAMPLES; i < 2 * NTRAINING_SAMPLES; ++i) {
|
||||
px = trainDataData[i * trainData.cols()];
|
||||
py = trainDataData[i * trainData.cols() + 1];
|
||||
Imgproc.circle(I, new Point(px, py), 3, new Scalar(255, 0, 0), thick, lineType, 0);
|
||||
}
|
||||
//! [show_data]
|
||||
|
||||
// ------------------------- 6. Show support vectors--------------------------------------------
|
||||
//! [show_vectors]
|
||||
thick = 2;
|
||||
Mat sv = svm.getUncompressedSupportVectors();
|
||||
float[] svData = new float[(int) (sv.total() * sv.channels())];
|
||||
sv.get(0, 0, svData);
|
||||
for (int i = 0; i < sv.rows(); i++) {
|
||||
Imgproc.circle(I, new Point(svData[i * sv.cols()], svData[i * sv.cols() + 1]), 6, new Scalar(128, 128, 128),
|
||||
thick, lineType, 0);
|
||||
}
|
||||
//! [show_vectors]
|
||||
|
||||
Imgcodecs.imwrite("result.png", I); // save the Image
|
||||
HighGui.imshow("SVM for Non-Linear Training Data", I); // show it to the user
|
||||
HighGui.waitKey();
|
||||
System.exit(0);
|
||||
}
|
||||
}
|
||||
Reference in New Issue
Block a user